Python 3 (ipykernel)
import torchimport torch.nn as nnimport timeimport argparseimport osimport datetimefrom torch.distributions.categorical import Categorical# visualization %matplotlib inlineimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdimport warningswarnings.filterwarnings("ignore", category=UserWarning)device = torch.device("cpu"); gpu_id = -1 # select CPUgpu_id = '0' # select a single GPU #gpu_id = '2,3' # select multiple GPUs os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) if torch.cuda.is_available(): device = torch.device("cuda") print('GPU name: {:s}, gpu_id: {:s}'.format(torch.cuda.get_device_name(0),gpu_id)) print(device)print('pytorch version = ',torch.__version__)GPU name: NVIDIA GeForce RTX 2080 Ti, gpu_id: 0 cuda pytorch version = 1.10.0
xxxxxxxxxxfrom config import *from utils.graph_utils import *from utils.google_tsp_reader import GoogleTSPReaderfrom utils.plot_utils import *from models.gcn_model import ResidualGatedGCNModelfrom utils.model_utils import *xxxxxxxxxxconfig_path = "configs/default.json"config = get_config(config_path)print("Loaded {}:\n{}".format(config_path, config))Loaded configs/default.json:
{'expt_name': 'deafult', 'gpu_id': '0', 'train_filepath': './data/tsp25_concorde.txt', 'val_filepath': './data/tsp25_concorde.txt', 'test_filepath': './data/tsp25_concorde.txt', 'num_nodes': 100, 'num_neighbors': 6, 'node_dim': 4, 'voc_nodes_in': 2, 'voc_nodes_out': 2, 'voc_edges_in': 3, 'voc_edges_out': 2, 'beam_size': 10, 'hidden_dim': 50, 'num_layers': 3, 'mlp_layers': 2, 'aggregation': 'mean', 'max_epochs': 1500, 'val_every': 5, 'test_every': 10, 'batch_size': 100, 'batches_per_epoch': 500, 'accumulation_steps': 1, 'learning_rate': 0.001, 'decay_rate': 1.01}
xxxxxxxxxxif torch.cuda.is_available(): print("CUDA available, using GPU ID {}".format(config.gpu_id)) dtypeFloat = torch.cuda.FloatTensor dtypeLong = torch.cuda.LongTensor torch.cuda.manual_seed(1)else: print("CUDA not available") dtypeFloat = torch.FloatTensor dtypeLong = torch.LongTensor torch.manual_seed(1)CUDA available, using GPU ID 0
xxxxxxxxxxnet = ResidualGatedGCNModel(config, dtypeFloat, dtypeLong)if torch.cuda.is_available(): net.cuda()# Define optimizerlearning_rate = config.learning_rateoptimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)xxxxxxxxxx# Load checkpointlog_dir = f"./logs/{config.expt_name}/"if torch.cuda.is_available(): checkpoint = torch.load(log_dir+"last_train_checkpoint.tar")else: checkpoint = torch.load(log_dir+"last_train_checkpoint.tar", map_location='cpu')# Load network statenet.load_state_dict(checkpoint['model_state_dict'])# Load optimizer stateoptimizer.load_state_dict(checkpoint['optimizer_state_dict'])# Load other training parametersepoch = checkpoint['epoch']train_loss = checkpoint['train_loss']val_loss = checkpoint['val_loss']for param_group in optimizer.param_groups: learning_rate = param_group['lr']print(f"Loaded checkpoint from epoch {epoch}")Loaded checkpoint from epoch 1499
xxxxxxxxxx# Set evaluation modefrom torch.autograd import Variablefrom sklearn.utils.class_weight import compute_class_weighttorch.backends.cudnn.enabled=Falsenet.eval()batch_size = 1num_nodes = config.num_nodesnum_neighbors = config.num_neighborsbeam_size = config.beam_sizetest_filepath = config.test_filepathdataset = iter(GoogleTSPReader(num_nodes, num_neighbors, batch_size, test_filepath))batch = next(dataset)with torch.no_grad(): # Convert batch to torch Variables x_edges = Variable(torch.LongTensor(batch.edges).type(dtypeLong), requires_grad=False) x_edges_values = Variable(torch.FloatTensor(batch.edges_values).type(dtypeFloat), requires_grad=False) x_nodes = Variable(torch.LongTensor(batch.nodes).type(dtypeLong), requires_grad=False) x_nodes_coord = Variable(torch.FloatTensor(batch.nodes_coord).type(dtypeFloat), requires_grad=False) y_edges = Variable(torch.LongTensor(batch.edges_target).type(dtypeLong), requires_grad=False) y_nodes = Variable(torch.LongTensor(batch.nodes_target).type(dtypeLong), requires_grad=False) # Compute class weights edge_labels = y_edges.cpu().numpy().flatten() edge_cw = compute_class_weight("balanced", classes=np.unique(edge_labels), y=edge_labels) print("Class weights: {}".format(edge_cw)) # Forward pass y_preds, loss = net.forward(x_edges, x_edges_values, x_nodes, x_nodes_coord, y_edges, edge_cw) loss = loss.mean() y = F.softmax(y_preds, dim=3) y_probs = y[:,:,:,1] y_table = y_probs[0] print(y_table[2]) # Plot prediction visualizations #plot_predictions_beamsearch(x_nodes_coord, x_edges, x_edges_values, y_edges, y_preds, num_plots=batch_size) Class weights: [ 0.50818173 31.05590062]
tensor([7.3542e-01, 7.3127e-04, 3.6397e-20, 3.1718e-01, 2.6180e-02, 2.4821e-01,
8.8011e-01, 2.7613e-02, 9.3657e-02, 7.7318e-01, 7.4782e-01, 7.0760e-02,
4.4032e-04, 5.8457e-04, 4.0113e-04, 3.0494e-04, 1.8628e-01, 1.1400e-01,
7.9371e-02, 5.0004e-02, 8.4600e-03, 1.2960e-02, 9.1731e-03, 6.1144e-03,
4.9338e-03, 1.2088e-02, 2.3081e-02, 6.5631e-03, 1.0479e-02, 1.0443e-02,
4.8727e-03, 4.7461e-03, 8.3459e-02, 1.0012e-01, 3.4546e-02, 3.7885e-02,
3.3079e-03, 5.4368e-03, 6.0076e-03, 3.9207e-03, 5.5807e-04, 6.8777e-04,
4.2805e-04, 3.4540e-04, 3.8895e-04, 4.6386e-04, 3.2650e-04, 2.7072e-04,
9.3666e-04, 1.1823e-03, 8.7744e-04, 6.8227e-04, 2.4374e-02, 3.0214e-02,
1.8023e-02, 1.4552e-02, 8.6380e-03, 8.6127e-03, 7.1083e-03, 8.8085e-03,
8.6509e-03, 6.2258e-03, 7.2163e-03, 5.4065e-03, 5.5713e-02, 1.5195e-01,
3.8148e-02, 1.4287e-01, 1.2525e-02, 1.6038e-02, 1.8765e-02, 1.5182e-02,
3.4367e-02, 3.7792e-02, 1.2517e-02, 1.1976e-02, 8.9101e-04, 1.2243e-03,
6.9761e-04, 5.1502e-04, 1.2569e-02, 2.4904e-02, 1.2555e-02, 1.7911e-02,
1.5525e-03, 1.9904e-03, 1.2533e-03, 9.6607e-04, 3.9319e-03, 4.0740e-03,
3.8087e-03, 3.6729e-03, 1.8649e-02, 2.3640e-02, 2.1168e-02, 1.6880e-02,
2.1226e-04, 3.0497e-04, 1.9573e-04, 1.3724e-04], device='cuda:0')
xxxxxxxxxxxxxxxxxxxximport mathimport numpy as npimport torch.nn.functional as Fimport randomimport torch.optim as optimfrom torch.autograd import Variablefrom torch.optim import lr_schedulerimport matplotlib.pyplot as pltfrom tqdm import tqdm_notebookclass TransEncoderNet(nn.Module): """ Encoder network based on self-attention transformer Inputs : h of size (bsz, nb_nodes, dim_emb) batch of input cities Outputs : h of size (bsz, nb_nodes, dim_emb) batch of encoded cities score of size (bsz, nb_nodes, nb_nodes+1) batch of attention scores """ def __init__(self, nb_layers, dim_emb, nb_heads, dim_ff, batchnorm): super(TransEncoderNet, self).__init__() assert dim_emb == nb_heads* (dim_emb//nb_heads) # check if dim_emb is divisible by nb_heads self.MHA_layers = nn.ModuleList( [nn.MultiheadAttention(dim_emb, nb_heads) for _ in range(nb_layers)] ) self.linear1_layers = nn.ModuleList( [nn.Linear(dim_emb, dim_ff) for _ in range(nb_layers)] ) self.linear2_layers = nn.ModuleList( [nn.Linear(dim_ff, dim_emb) for _ in range(nb_layers)] ) if batchnorm: self.norm1_layers = nn.ModuleList( [nn.BatchNorm1d(dim_emb) for _ in range(nb_layers)] ) self.norm2_layers = nn.ModuleList( [nn.BatchNorm1d(dim_emb) for _ in range(nb_layers)] ) else: self.norm1_layers = nn.ModuleList( [nn.LayerNorm(dim_emb) for _ in range(nb_layers)] ) self.norm2_layers = nn.ModuleList( [nn.LayerNorm(dim_emb) for _ in range(nb_layers)] ) self.nb_layers = nb_layers self.nb_heads = nb_heads self.batchnorm = batchnorm def forward(self, h): # PyTorch nn.MultiheadAttention requires input size (seq_len, bsz, dim_emb) h = h.transpose(0,1) # size(h)=(nb_nodes, bsz, dim_emb) # L layers for i in range(self.nb_layers): h_rc = h # residual connection, size(h_rc)=(nb_nodes, bsz, dim_emb) h, score = self.MHA_layers[i](h, h, h) # size(h)=(nb_nodes, bsz, dim_emb), size(score)=(bsz, nb_nodes, nb_nodes) # add residual connection h = h_rc + h # size(h)=(nb_nodes, bsz, dim_emb) if self.batchnorm: # Pytorch nn.BatchNorm1d requires input size (bsz, dim, seq_len) h = h.permute(1,2,0).contiguous() # size(h)=(bsz, dim_emb, nb_nodes) h = self.norm1_layers[i](h) # size(h)=(bsz, dim_emb, nb_nodes) h = h.permute(2,0,1).contiguous() # size(h)=(nb_nodes, bsz, dim_emb) else: h = self.norm1_layers[i](h) # size(h)=(nb_nodes, bsz, dim_emb) # feedforward h_rc = h # residual connection h = self.linear2_layers[i](torch.relu(self.linear1_layers[i](h))) h = h_rc + h # size(h)=(nb_nodes, bsz, dim_emb) if self.batchnorm: h = h.permute(1,2,0).contiguous() # size(h)=(bsz, dim_emb, nb_nodes) h = self.norm2_layers[i](h) # size(h)=(bsz, dim_emb, nb_nodes) h = h.permute(2,0,1).contiguous() # size(h)=(nb_nodes, bsz, dim_emb) else: h = self.norm2_layers[i](h) # size(h)=(nb_nodes, bsz, dim_emb) # Transpose h h = h.transpose(0,1) # size(h)=(bsz, nb_nodes, dim_emb) return h, score class Attention(nn.Module): def __init__(self, n_hidden): super(Attention, self).__init__() self.size = 0 self.batch_size = 0 self.dim = n_hidden v = torch.FloatTensor(n_hidden) self.v = nn.Parameter(v) self.v.data.uniform_(-1/math.sqrt(n_hidden), 1/math.sqrt(n_hidden)) # parameters for pointer attention self.Wref = nn.Linear(n_hidden, n_hidden) self.Wq = nn.Linear(n_hidden, n_hidden) def forward(self, q, ref): # query and reference self.batch_size = q.size(0) self.size = int(ref.size(0) / self.batch_size) q = self.Wq(q) # (B, dim) ref = self.Wref(ref) ref = ref.view(self.batch_size, self.size, self.dim) # (B, size, dim) q_ex = q.unsqueeze(1).repeat(1, self.size, 1) # (B, size, dim) # v_view: (B, dim, 1) v_view = self.v.unsqueeze(0).expand(self.batch_size, self.dim).unsqueeze(2) # (B, size, dim) * (B, dim, 1) u = torch.bmm(torch.tanh(q_ex + ref), v_view).squeeze(2) return u, ref class LSTM(nn.Module): def __init__(self, n_hidden): super(LSTM, self).__init__() # parameters for input gate self.Wxi = nn.Linear(n_hidden, n_hidden) # W(xt) self.Whi = nn.Linear(n_hidden, n_hidden) # W(ht) self.wci = nn.Linear(n_hidden, n_hidden) # w(ct) # parameters for forget gate self.Wxf = nn.Linear(n_hidden, n_hidden) # W(xt) self.Whf = nn.Linear(n_hidden, n_hidden) # W(ht) self.wcf = nn.Linear(n_hidden, n_hidden) # w(ct) # parameters for cell gate self.Wxc = nn.Linear(n_hidden, n_hidden) # W(xt) self.Whc = nn.Linear(n_hidden, n_hidden) # W(ht) # parameters for forget gate self.Wxo = nn.Linear(n_hidden, n_hidden) # W(xt) self.Who = nn.Linear(n_hidden, n_hidden) # W(ht) self.wco = nn.Linear(n_hidden, n_hidden) # w(ct) def forward(self, x, h, c): # query and reference # input gate i = torch.sigmoid(self.Wxi(x) + self.Whi(h) + self.wci(c)) # forget gate f = torch.sigmoid(self.Wxf(x) + self.Whf(h) + self.wcf(c)) # cell gate c = f * c + i * torch.tanh(self.Wxc(x) + self.Whc(h)) # output gate o = torch.sigmoid(self.Wxo(x) + self.Who(h) + self.wco(c)) h = o * torch.tanh(c) return h, cclass HPN(nn.Module): def __init__(self, n_feature, n_hidden): super(HPN, self).__init__() self.city_size = 0 self.batch_size = 0 self.dim = n_hidden # lstm for first turn #self.lstm0 = nn.LSTM(n_hidden, n_hidden)main_training - Jupyter Notebook # pointer layer self.TransPointer = Attention(n_hidden) # lstm encoder self.encoder = LSTM(n_hidden) # trainable first hidden input h0 = torch.FloatTensor(n_hidden) c0 = torch.FloatTensor(n_hidden) # trainable latent variable coefficient print('here') alpha = torch.ones(1) self.h0 = nn.Parameter(h0) self.c0 = nn.Parameter(c0) self.alpha = nn.Parameter(alpha) self.h0.data.uniform_(-1/math.sqrt(n_hidden), 1/math.sqrt(n_hidden)) self.c0.data.uniform_(-1/math.sqrt(n_hidden), 1/math.sqrt(n_hidden)) # embedding self.embedding_x = nn.Linear(n_feature, n_hidden) self.embedding_all = nn.Linear(n_feature, n_hidden) self.Transembedding_all = TransEncoderNet(6, 128, 8, 512, batchnorm=True)#6,128,8,512 # vector to start decoding self.start_placeholder = nn.Parameter(torch.randn(n_hidden)) def forward(self,context,Transcontext, x, X_all, mask,choosen_idx,GCN_table,if_check_u, h=None, c=None, latent=None): ''' Inputs (B: batch size, size: city size, dim: hidden dimension) x: current city coordinate (B, 2) X_all: all cities' cooridnates (B, size, 2) mask: mask visited cities h: hidden variable (B, dim) c: cell gate (B, dim) latent: latent pointer vector from previous layer (B, size, dim) Outputs softmax: probability distribution of next city (B, size) h: hidden variable (B, dim) c: cell gate (B, dim) latent_u: latent pointer vector for next layer ''' self.batch_size = X_all.size(0) self.city_size = X_all.size(1) zero_to_bsz = torch.arange(self.batch_size, device=device) # Check if this the first iteration loop if h is None or c is None: x = self.start_placeholder context = self.embedding_all(X_all) Transcontext,_ = self.Transembedding_all(context) # (B, size, dim) Transcontext = Transcontext.reshape(-1, self.dim) h0 = self.h0.unsqueeze(0).expand(self.batch_size, self.dim) c0 = self.c0.unsqueeze(0).expand(self.batch_size, self.dim) h0 = h0.unsqueeze(0).contiguous() c0 = c0.unsqueeze(0).contiguous() # let h0, c0 be the hidden variable of first turn h = h0.squeeze(0) c = c0.squeeze(0) else: x = self.embedding_x(x) # LSTM encoder1900/2500 h, c = self.encoder(x, h, c) # query vector q = h # pointer u2 ,_ = self.TransPointer(q, Transcontext) # Avg Agg between the two attention vector u = u2 latent_u = u.clone() # Add the GCN part, we have a matrix n*n n = number of points u = 10 * torch.tanh(u) if choosen_idx is None: pass else: GCN_advice = GCN_table[zero_to_bsz, choosen_idx] #u = u*GCN_advice if if_check_u is True: print(u[0]) u = u + mask return context, Transcontext,F.softmax(u, dim=1), h, c, latent_u# params= list(HPN(n_feature = 2, n_hidden = 128).parameters())# print(len(params))# for name,parameters in HPN(n_feature = 2, n_hidden = 128).named_parameters():# print(name,':',parameters.size())xxxxxxxxxx#test network Actor = HPN(n_feature = 2, n_hidden = 128)print(Actor)nb_param = 0for param in Actor.parameters(): nb_param += np.prod(list(param.data.size()))print('Number of parameters:', nb_param)here
HPN(
(TransPointer): Attention(
(Wref): Linear(in_features=128, out_features=128, bias=True)
(Wq): Linear(in_features=128, out_features=128, bias=True)
)
(encoder): LSTM(
(Wxi): Linear(in_features=128, out_features=128, bias=True)
(Whi): Linear(in_features=128, out_features=128, bias=True)
(wci): Linear(in_features=128, out_features=128, bias=True)
(Wxf): Linear(in_features=128, out_features=128, bias=True)
(Whf): Linear(in_features=128, out_features=128, bias=True)
(wcf): Linear(in_features=128, out_features=128, bias=True)
(Wxc): Linear(in_features=128, out_features=128, bias=True)
(Whc): Linear(in_features=128, out_features=128, bias=True)
(Wxo): Linear(in_features=128, out_features=128, bias=True)
(Who): Linear(in_features=128, out_features=128, bias=True)
(wco): Linear(in_features=128, out_features=128, bias=True)
)
(embedding_x): Linear(in_features=2, out_features=128, bias=True)
(embedding_all): Linear(in_features=2, out_features=128, bias=True)
(Transembedding_all): TransEncoderNet(
(MHA_layers): ModuleList(
(0): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
(1): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
(2): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
(3): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
(4): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
(5): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
)
(linear1_layers): ModuleList(
(0): Linear(in_features=128, out_features=512, bias=True)
(1): Linear(in_features=128, out_features=512, bias=True)
(2): Linear(in_features=128, out_features=512, bias=True)
(3): Linear(in_features=128, out_features=512, bias=True)
(4): Linear(in_features=128, out_features=512, bias=True)
(5): Linear(in_features=128, out_features=512, bias=True)
)
(linear2_layers): ModuleList(
(0): Linear(in_features=512, out_features=128, bias=True)
(1): Linear(in_features=512, out_features=128, bias=True)
(2): Linear(in_features=512, out_features=128, bias=True)
(3): Linear(in_features=512, out_features=128, bias=True)
(4): Linear(in_features=512, out_features=128, bias=True)
(5): Linear(in_features=512, out_features=128, bias=True)
)
(norm1_layers): ModuleList(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(norm2_layers): ModuleList(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
Number of parameters: 1405569
xxxxxxxxxx'''This part I designed the rectangle-characterized TSP, that means for every step the agent walk through a corner,then he travel through the whole rectangle using zig-zag, finally he ends up at one of the rest corners of the rextangle, so, it equals the agent walk through three points at one step, in practice, I add three points into mask to make them unselectable.'''def rectangle_process(temp,idx,Y,Y0,mask,k,B,i,path_gazebo, visit_count, visit_time_count, if_actor): Y1 = Y[zero_to_bsz, idx.data].clone() rectangle_inf = idx/4 feature_table = temp.outcorner_getout(rectangle_inf,B) feature_table = torch.Tensor(feature_table).type(torch.long) if torch.cuda.is_available(): feature_table = feature_table.cuda() Y_corner = Y[zero_to_bsz, feature_table[:,0].data].clone() add_time = visit_time_count[zero_to_bsz, feature_table[:,0].data]*((range_of_wait/2)*speed_of_nozzle) if torch.cuda.is_available(): add_time = add_time.cuda() Y_corner[:,dimension-2] = add_time if (i % 100 == 0)and(if_actor): path_gazebo.append([idx.data[0].tolist(),feature_table[:,0].data[0].tolist()]) if k ==0: if torch.cuda.is_available(): reward = torch.zeros(B).cuda() else: reward = torch.zeros(B) if k > 0: reward = torch.sum((Y1[:,(0,1)] - Y0[:,(0,1)])**2 , dim=1 )**0.5 reward += torch.sum((Y_corner[:,(0,1)] - Y1[:,(0,1)])**2 , dim=1 )**0.5 #dis = (Y1 - Y0)**2 #dis_1 = (Y_corner - Y1)**2 #reward = torch.maximum(dis[:,0],dis[:,1])**0.5 #reward += torch.maximum(dis_1[:,0],dis_1[:,1])**0.5 visit_count[zero_to_bsz, idx.data] -= 1 visit_count[zero_to_bsz, feature_table[:,0].data] -= 1 visit_count[zero_to_bsz, feature_table[:,1].data] -= 1 visit_count[zero_to_bsz, feature_table[:,2].data] -= 1 visit_time_count[zero_to_bsz, idx.data] += 1 visit_time_count[zero_to_bsz, feature_table[:,0].data] += 1 visit_time_count[zero_to_bsz, feature_table[:,1].data] += 1 visit_time_count[zero_to_bsz, feature_table[:,2].data] += 1 if_revisited = visit_count[zero_to_bsz, idx.data]<=0 if_revisited = -np.inf*if_revisited if_revisited[if_revisited!=if_revisited] = 0 mask[zero_to_bsz, idx.data] += if_revisited mask[zero_to_bsz, feature_table[:,0].data] += if_revisited mask[zero_to_bsz, feature_table[:,1].data ] += if_revisited mask[zero_to_bsz, feature_table[:,2].data ] += if_revisited return Y, reward, Y_corner, Y_corner,feature_table[:,0]xxxxxxxxxximport yamlfrom torch.distributions.categorical import Categorical# visualization%matplotlib inlineimport matplotlib.pyplot as pltimport warningsfrom tqdm import tqdm_notebookimport torch.nn.functional as F####### my own import file ##########from listofpathpoint import input_handlerimport cnc_inputimport copy#from hybrid_models import HPN####### my own import file ##########'''so, the models we have are TransEncoderNet, Attention LSTM HPNeach one have initial parameters and the forward part, once we have the forward part, the back propagation will finished automatically by pytorch '''TOL = 1e-3TINY = 1e-15learning_rate = 1e-4 #learning rateB = 128 #batch sizeB_valLoop = 1steps = 2500n_epoch = 100 # epochsmap_number = 0record_actor = []record_critic = []dimension = 4speed_of_nozzle = 30range_of_wait = 20scale_of_the_map = (800,500,speed_of_nozzle*range_of_wait)print('======================')print('prepare to train')print('======================')print('Hyper parameters:')print('learning rate', learning_rate)print('batch size', B)print('steps', steps)print('epoch', n_epoch)print('======================')'''instantiate a training network and a baseline network'''try: del Actor # remove existing model del Critic # remove existing modelexcept: passActor = HPN(n_feature = dimension, n_hidden = 128)Critic = HPN(n_feature = dimension, n_hidden = 128)optimizer = optim.Adam(Actor.parameters(), lr=learning_rate)# Putting Critic model on the eval modeActor = Actor.to(device)Critic = Critic.to(device)Critic.eval()epoch_ckpt = 0tot_time_ckpt = 0val_mean = []val_std = []maplist = ['10&15data/25_chips/25_1.json' ,'10&15data/25_chips/25_1.json','10&15data/25_chips/25_1.json'\ ,'10&15data/25_chips/25_1.json','10&15data/25_chips/25_1.json'\ ,'10&15data/25_chips/25_1.json' ] plot_performance_train = []plot_performance_baseline = []# recording the result of the resent epoch makes it available for future#*********************# Uncomment these lines to load the previous check point"""checkpoint_file = "checkpoint/mutimap_20.pkl"checkpoint = torch.load(checkpoint_file, map_location=device)epoch_ckpt = checkpoint['epoch'] + 1tot_time_ckpt = checkpoint['tot_time']plot_performance_train = checkpoint['plot_performance_train']plot_performance_baseline = checkpoint['plot_performance_baseline']Critic.load_state_dict(checkpoint['model_baseline'])Actor.load_state_dict(checkpoint['model_train'])optimizer.load_state_dict(checkpoint['optimizer'])print('Re-start training with saved checkpoint file={:s}\n Checkpoint at epoch= {:d} and time={:.3f}min\n'.format(checkpoint_file,epoch_ckpt-1,tot_time_ckpt/60))"""#***********************# Uncomment these lines to load the previous check point# Main training loop# The core training concept mainly upon Sampling from the actor# then taking the greedy action from the criticstart_training_time = time.time()time_stamp = datetime.datetime.now().strftime("%y-%m-%d--%H-%M-%S") # Load the time stampC = 0 # baseline => the object which the actor can compareR = 0 # rewardC_critic = torch.Tensor([12000])if torch.cuda.is_available(): C_critic = C_critic.cuda()temp = input_handler(maplist[map_number])X_temp, mask_list_num = temp.final_ver_points()print(X_temp)print(mask_list_num)#change the waiting time to the distance that we can measuresize = len(X_temp)zero_to_bsz = torch.arange(B, device = device) # a list contains 0 to (batch size -1)# load the GCN tableGCN_table = y_table.repeat(B,1,1)for corner in X_temp: corner[dimension -2] *= speed_of_nozzlesize_rec = mask_list_num[-1]print(size_rec)X_temp = torch.FloatTensor(X_temp)visit_count_initial = X_temp[:,3]visit_count_rec = []for i in range(0,len(visit_count_initial),4): visit_count_rec.append(int(visit_count_initial[i]))X = X_temp.repeat(B,1,1)if torch.cuda.is_available(): X = X.cuda()for epoch in range(0, n_epoch): # re-start training with saved checkpoint epoch += epoch_ckpt # adding the number of the former epochs # Train the model for one epoch start = time.time() # record the starting time Actor.train() path_gazebo = [] for i_all in range(1, steps+1): # 1 ~ 2500 steps # mask some points that are not the first visited points if torch.cuda.is_available(): R = torch.zeros(B).cuda() reward_recorder = torch.zeros(B,size_rec).cuda() idx_recorder = torch.zeros(B,size_rec).cuda() logprobs = 0 reward = torch.zeros(B).cuda() mask = torch.zeros(B,size).cuda() # use mask to make some points impossible to choose else: R = torch.zeros(B) reward_recorder = torch.zeros(B,size_rec) idx_recorder = torch.zeros(B,size_rec) logprobs = 0 reward = torch.zeros(B) mask = torch.zeros(B,size) # use mask to make some points impossible to choose x = torch.zeros(B,dimension)# Y[:,0,:] #set the first point to x h = None c = None context = None # set Y_ini to the out corner Transcontext = None Y0 = None choosen_idx = None visit_count = visit_count_initial.repeat(B,1) visit_time_count_initial = torch.zeros(len(visit_count_initial)) visit_time_count = visit_time_count_initial.repeat(B,1) if torch.cuda.is_available(): visit_count = visit_count.cuda() # Actor Sampling phase for k in range(size_rec): if k ==0: Y = X.view(B,size,dimension) context, Transcontext, output, h, c, _ = Actor(context,Transcontext,x=x, X_all=X,h=h, c=c, mask=mask, choosen_idx=choosen_idx, GCN_table=GCN_table,if_check_u=False) sampler = torch.distributions.Categorical(output) idx = sampler.sample() idx_recorder[:,k] = (idx/4).type(torch.long) #prepare for the back propagation of pytorch Y, reward, Y0, x, choosen_idx = rectangle_process(temp, idx,Y,Y0,mask,k,B,i_all,path_gazebo, visit_count, visit_time_count, if_actor=True) R += reward reward_recorder[:,k] = R logprobs += torch.log(output[zero_to_bsz, idx.data] + TINY) #now is the time to check if any trajectory should be punished due to waiting on the same rectangle trajec_count = 0 for path_time in zip(idx_recorder,reward_recorder): idx_time = zip(path_time[0],path_time[1]) idx_time = sorted(idx_time, key=lambda x: x[0]) total_idx = 0 extra_waiting_time = 0 for item in visit_count_rec: compare_list = [] if item>1: for i in range(int(item)): compare_list.append(idx_time[int(total_idx + i)][1]) compare_list.sort(reverse=True) total_idx += item for i in range(len(compare_list)-1): dis_step = compare_list[i] - compare_list[i+1] dry_time = (range_of_wait/2)*speed_of_nozzle if (dis_step) < (dry_time): extra_waiting_time += dry_time - dis_step else: total_idx += item R[trajec_count] += extra_waiting_time trajec_count+=1 # # critic baseline phase, use the baseline to compute the actual reward of agent at that time# if torch.cuda.is_available():# C = torch.zeros(B).cuda()# reward_recorder = torch.zeros(B,size_rec).cuda()# idx_recorder = torch.zeros(B,size_rec).cuda()# baseline = torch.zeros(B).cuda()# mask = torch.zeros(B,size).cuda() # use mask to make some points impossible to choose# else:# C = torch.zeros(B)# reward_recorder = torch.zeros(B,size_rec)# idx_recorder = torch.zeros(B,size_rec)# baseline = torch.zeros(B)# mask = torch.zeros(B,size) # use mask to make some points impossible to choose# x = torch.zeros(B,dimension)#Y[:,0,:] #set the first point to x# h = None# c = None# context = None# Transcontext = None# C0 = None# visit_count = visit_count_initial.repeat(B,1)# if torch.cuda.is_available():# visit_count = visit_count.cuda()# # compute tours for baseline without grad "Cause we want to fix the weights for the critic"# with torch.no_grad():# for k in range(size_rec): # Y = X.view(B,size,dimension)# #check if all the points to be masked,if so, raise the total reward R, and check again# context, Transcontext, output, h, c, _ = Critic(context,Transcontext,x=x, X_all=X,h=h,# c=c, mask=mask)# idx = torch.argmax(output, dim=1) # ----> greedy baseline critic# idx_recorder[:,k] = (idx/4).type(torch.long)# # prepare for the back propagation of pytorch# baseline, C0,x = rectangle_process(temp,idx,Y,C0, mask,k,B,i_all,path_gazebo,# visit_count,if_actor=False)# C += baseline# reward_recorder[:,k] = C# #now is the time to check if any trajectory should be punished due to waiting on the same rectangle# trajec_count = 0# for path_time in zip(idx_recorder,reward_recorder):# idx_time = zip(path_time[0],path_time[1])# idx_time = sorted(idx_time, key=lambda x: x[0])# total_idx = 0# extra_waiting_time = 0# for item in visit_count_rec:# compare_list = []# if item>1:# for i in range(int(item)):# compare_list.append(idx_time[int(total_idx + i)][1])# compare_list.sort(reverse=True)# total_idx += item# for i in range(len(compare_list)-1):# dis_step = compare_list[i] - compare_list[i+1]# dry_time = (range_of_wait/2)*speed_of_nozzle# if (dis_step) < (dry_time):# extra_waiting_time += dry_time - dis_step# else:# total_idx += item# C[trajec_count] += extra_waiting_time# trajec_count+=1 ################### # Loss and backprop handling ################### loss = torch.mean((R - C_critic[0]) * logprobs) optimizer.zero_grad() loss.backward() optimizer.step() if i_all % 100 == 0: print("epoch:{}, batch:{}/{}, reward:{}".format(epoch, i_all, steps,R.mean().item())) record_actor.append(R.mean().tolist()) record_critic.append(C_critic[0].tolist()) plt.plot(record_actor,'r:') plt.plot(record_critic,'b:') plt.show() if i_all % 100 == 0: print("record the last path to gazebo for showing up") #starting to show the path on simulated enviroment of cnc_machine the_resent_path = temp.zig_zag_path(path_gazebo,mask_list_num) data = {'path':the_resent_path} data_1 = {'corners':path_gazebo} pathpoints_dir = os.path.join("pathpoints") if not os.path.exists(pathpoints_dir): os.makedirs(pathpoints_dir) name = 'pathpoints/path_points '+str(i)+'.yaml' with open(name, 'w') as file: documents = yaml.dump(data,file) documents = yaml.dump(data_1,file) path_gazebo = [] time_one_epoch = time.time() - start #recording the work time of one epoch time_tot = time.time() - start_training_time + tot_time_ckpt ################### # Evaluate train model and baseline # in this phase we just solve random instances with the actor and the critic # compare this soluation if we get any improvment we'll transfer the actor's # weights into the critic ################### # putting the actor in the eval mode Actor.eval() mean_tour_length_actor = 0 mean_tour_length_critic = 0 for step in range(0,B_valLoop): # compute tour for model and baseline # mask some points that are not the first visited points if torch.cuda.is_available(): R = torch.zeros(B).cuda() reward_recorder = torch.zeros(B,size_rec).cuda() idx_recorder = torch.zeros(B,size_rec).cuda() reward = torch.zeros(B).cuda() mask = torch.zeros(B,size).cuda() # use mask to make some points impossible to choose else: R = torch.zeros(B) reward_recorder = torch.zeros(B,size_rec) idx_recorder = torch.zeros(B,size_rec) reward = torch.zeros(B) mask = torch.zeros(B,size) # use mask to make some points impossible to choose x = torch.zeros(B,dimension)# Y[:,0,:] #set the first point to x h = None c = None context = None # set Y_ini to the out corner Transcontext = None Y0 = None choosen_idx = None visit_count = visit_count_initial.repeat(B,1) visit_time_count_initial = torch.zeros(len(visit_count_initial)) visit_time_count = visit_time_count_initial.repeat(B,1) if torch.cuda.is_available(): visit_count = visit_count.cuda() with torch.no_grad(): for k in range(size_rec): if k==0: Y = X.view(B,size,dimension) context, Transcontext, output, h, c, _ = Actor(context,Transcontext,x=x, X_all=X,h=h, c=c, mask=mask,choosen_idx=choosen_idx,GCN_table=GCN_table,if_check_u=True) idx = torch.argmax(output, dim=1) idx_recorder[:,k] = (idx/4).type(torch.long) #prepare for the back propagation of pytorch Y, reward, Y0, x, choosen_idx = rectangle_process(temp, idx,Y,Y0,mask,k,B,i,path_gazebo, visit_count, visit_time_count, if_actor=False) R += reward reward_recorder[:,k] = R #now is the time to check if any trajectory should be punished due to waiting on the same rectangle trajec_count = 0 for path_time in zip(idx_recorder,reward_recorder): idx_time = zip(path_time[0],path_time[1]) idx_time = sorted(idx_time, key=lambda x: x[0]) total_idx = 0 extra_waiting_time = 0 for item in visit_count_rec: compare_list = [] if item>1: for i in range(int(item)): compare_list.append(idx_time[int(total_idx + i)][1]) compare_list.sort(reverse=True) total_idx += item for i in range(len(compare_list)-1): dis_step = compare_list[i] - compare_list[i+1] dry_time = (range_of_wait/2)*speed_of_nozzle if (dis_step) < (dry_time): extra_waiting_time += dry_time - dis_step else: total_idx += item R[trajec_count] += extra_waiting_time trajec_count+=1 print('R_val = ',R[0]) if torch.cuda.is_available(): C = torch.zeros(B).cuda() reward_recorder = torch.zeros(B,size_rec).cuda() idx_recorder = torch.zeros(B,size_rec).cuda() baseline = torch.zeros(B).cuda() mask = torch.zeros(B,size).cuda() # use mask to make some points impossible to choose else: C = torch.zeros(B) reward_recorder = torch.zeros(B,size_rec) idx_recorder = torch.zeros(B,size_rec) baseline = torch.zeros(B) mask = torch.zeros(B,size) # use mask to make some points impossible to choose x = torch.zeros(B,dimension)#Y[:,0,:] #set the first point to x h = None c = None context = None Transcontext = None C0 = None choosen_idx = None visit_count = visit_count_initial.repeat(B,1) visit_time_count_initial = torch.zeros(len(visit_count_initial)) visit_time_count = visit_time_count_initial.repeat(B,1) if torch.cuda.is_available(): visit_count = visit_count.cuda() # compute tours for baseline without grad "Cause we want to fix the weights for the critic" with torch.no_grad(): for k in range(size_rec): if k ==0: Y = X.view(B,size,dimension) #check if all the points to be masked,if so, raise the total reward R, and check again context, Transcontext, output, h, c, _ = Critic(context,Transcontext,x=x, X_all=X,h=h, c=c, mask=mask, choosen_idx=choosen_idx, GCN_table=GCN_table, if_check_u=False) idx = torch.argmax(output, dim=1) # ----> greedy baseline critic idx_recorder[:,k] = (idx/4).type(torch.long) # prepare for the back propagation of pytorch Y, baseline, C0, x, choosen_idx = rectangle_process(temp,idx,Y,C0, mask,k,B,i,path_gazebo, visit_count, visit_time_count, if_actor=False) C += baseline reward_recorder[:,k] = C #now is the time to check if any trajectory should be punished due to waiting on the same rectangle trajec_count = 0 for path_time in zip(idx_recorder,reward_recorder): idx_time = zip(path_time[0],path_time[1]) idx_time = sorted(idx_time, key=lambda x: x[0]) total_idx = 0 extra_waiting_time = 0 for item in visit_count_rec: compare_list = [] if item>1: for i in range(int(item)): compare_list.append(idx_time[int(total_idx + i)][1]) compare_list.sort(reverse=True) total_idx += item for i in range(len(compare_list)-1): dis_step = compare_list[i] - compare_list[i+1] dry_time = (range_of_wait/2)*speed_of_nozzle if (dis_step) < (dry_time): extra_waiting_time += dry_time - dis_step else: total_idx += item C[trajec_count] += extra_waiting_time trajec_count+=1 print('C_val = ',C[0]) C_critic = [C[0]] mean_tour_length_actor += R.mean().item() mean_tour_length_critic += C.mean().item() mean_tour_length_actor = mean_tour_length_actor / B_valLoop mean_tour_length_critic = mean_tour_length_critic / B_valLoop # evaluate train model and baseline and update if train model is better update_baseline = mean_tour_length_actor + TOL < mean_tour_length_critic print('Avg Actor {} --- Avg Critic {}'.format(mean_tour_length_actor,mean_tour_length_critic)) if update_baseline: Critic.load_state_dict(Actor.state_dict()) C_critic = [R[0]] print('My actor is going on the right road Hallelujah :) Updated') ################### # Valdiation train model and baseline on 1k random TSP instances ################### # erased by daniel due to the 1K tsp is not the scale I want to train # For checkpoint plot_performance_train.append([(epoch+1), mean_tour_length_actor]) plot_performance_baseline.append([(epoch+1), mean_tour_length_critic]) # compute the optimally gap ==> this is interesting because there is no LKH or other optimal algorithms # for the problem like this rectangle characterized map mystring_min = 'Epoch: {:d}, epoch time: {:.3f}min, tot time: {:.3f}day, L_actor: {:.3f}, L_critic: {:.3f}, update: {}'.format( epoch, time_one_epoch/60, time_tot/86400, mean_tour_length_actor, mean_tour_length_critic, update_baseline) print(mystring_min) print('Save Checkpoints') # Saving checkpoint checkpoint_dir = os.path.join("checkpoint") if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) torch.save({ 'epoch': epoch, 'time': time_one_epoch, 'tot_time': time_tot, 'loss': loss.item(), 'plot_performance_train': plot_performance_train, 'plot_performance_baseline': plot_performance_baseline, 'model_baseline': Critic.state_dict(), 'model_train': Actor.state_dict(), 'optimizer': optimizer.state_dict(), },'{}.pkl'.format(checkpoint_dir + "/checkpoint_" + time_stamp + "-n{}".format(size) + "-gpu{}".format(gpu_id))) ====================== prepare to train ====================== Hyper parameters: learning rate 0.0001 batch size 128 steps 2500 epoch 100 ====================== here here [[533, 152, 0, 1], [633, 152, 0, 1], [633, 166, 0, 1], [533, 166, 0, 1], [198, 80, 0, 2], [236, 80, 0, 2], [236, 105, 0, 2], [198, 105, 0, 2], [171, 280, 0, 1], [213, 280, 0, 1], [213, 311, 0, 1], [171, 311, 0, 1], [410, 122, 0, 3], [436, 122, 0, 3], [436, 134, 0, 3], [410, 134, 0, 3], [367, 71, 0, 3], [384, 71, 0, 3], [384, 83, 0, 3], [367, 83, 0, 3], [592, 450, 0, 3], [687, 450, 0, 3], [687, 462, 0, 3], [592, 462, 0, 3], [747, 131, 0, 3], [777, 131, 0, 3], [777, 148, 0, 3], [747, 148, 0, 3], [332, 413, 0, 1], [377, 413, 0, 1], [377, 443, 0, 1], [332, 443, 0, 1], [513, 231, 0, 1], [542, 231, 0, 1], [542, 260, 0, 1], [513, 260, 0, 1], [158, 120, 0, 3], [188, 120, 0, 3], [188, 234, 0, 3], [158, 234, 0, 3], [299, 138, 0, 2], [332, 138, 0, 2], [332, 251, 0, 2], [299, 251, 0, 2], [357, 287, 0, 2], [396, 287, 0, 2], [396, 318, 0, 2], [357, 318, 0, 2], [277, 480, 0, 3], [294, 480, 0, 3], [294, 491, 0, 3], [277, 491, 0, 3], [698, 202, 0, 3], [754, 202, 0, 3], [754, 223, 0, 3], [698, 223, 0, 3], [612, 224, 0, 1], [658, 224, 0, 1], [658, 258, 0, 1], [612, 258, 0, 1], [421, 241, 0, 1], [475, 241, 0, 1], [475, 261, 0, 1], [421, 261, 0, 1], [442, 70, 0, 2], [461, 70, 0, 2], [461, 83, 0, 2], [442, 83, 0, 2], [436, 444, 0, 2], [490, 444, 0, 2], [490, 455, 0, 2], [436, 455, 0, 2], [244, 378, 0, 3], [264, 378, 0, 3], [264, 389, 0, 3], [244, 389, 0, 3], [284, 78, 0, 2], [326, 78, 0, 2], [326, 109, 0, 2], [284, 109, 0, 2], [724, 86, 0, 3], [743, 86, 0, 3], [743, 99, 0, 3], [724, 99, 0, 3], [469, 190, 0, 2], [569, 190, 0, 2], [569, 204, 0, 2], [469, 204, 0, 2], [479, 373, 0, 2], [518, 373, 0, 2], [518, 405, 0, 2], [479, 405, 0, 2], [631, 338, 0, 3], [677, 338, 0, 3], [677, 372, 0, 3], [631, 372, 0, 3], [244, 225, 0, 3], [277, 225, 0, 3], [277, 338, 0, 3], [244, 338, 0, 3]] [0, 1, 3, 4, 7, 10, 13, 16, 17, 18, 21, 23, 25, 28, 31, 32, 33, 35, 37, 40, 42, 45, 47, 49, 52, 55] 55 epoch:0, batch:100/2500, reward:11903.890625
record the last path to gazebo for showing up
epoch:0, batch:200/2500, reward:11579.59765625
record the last path to gazebo for showing up
epoch:0, batch:300/2500, reward:11287.205078125
record the last path to gazebo for showing up
epoch:0, batch:400/2500, reward:11163.1015625
record the last path to gazebo for showing up
epoch:0, batch:500/2500, reward:11204.2099609375
record the last path to gazebo for showing up
epoch:0, batch:600/2500, reward:11107.5029296875
record the last path to gazebo for showing up
epoch:0, batch:700/2500, reward:11101.7138671875
record the last path to gazebo for showing up
epoch:0, batch:800/2500, reward:10968.7216796875
record the last path to gazebo for showing up
epoch:0, batch:900/2500, reward:10813.8291015625
record the last path to gazebo for showing up
epoch:0, batch:1000/2500, reward:10803.876953125
record the last path to gazebo for showing up
epoch:0, batch:1100/2500, reward:10637.8349609375
record the last path to gazebo for showing up
epoch:0, batch:1200/2500, reward:10646.5732421875
record the last path to gazebo for showing up
epoch:0, batch:1300/2500, reward:10587.828125
record the last path to gazebo for showing up
epoch:0, batch:1400/2500, reward:10446.916015625
record the last path to gazebo for showing up
epoch:0, batch:1500/2500, reward:10264.716796875
record the last path to gazebo for showing up
epoch:0, batch:1600/2500, reward:10426.7744140625
record the last path to gazebo for showing up
epoch:0, batch:1700/2500, reward:10179.3310546875
record the last path to gazebo for showing up
epoch:0, batch:1800/2500, reward:10743.685546875
record the last path to gazebo for showing up
epoch:0, batch:1900/2500, reward:10264.298828125
record the last path to gazebo for showing up
epoch:0, batch:2000/2500, reward:10336.93359375
record the last path to gazebo for showing up
epoch:0, batch:2100/2500, reward:10287.953125
record the last path to gazebo for showing up
epoch:0, batch:2200/2500, reward:10059.0146484375
record the last path to gazebo for showing up
epoch:0, batch:2300/2500, reward:10332.5634765625
record the last path to gazebo for showing up
epoch:0, batch:2400/2500, reward:10137.23046875
record the last path to gazebo for showing up
epoch:0, batch:2500/2500, reward:10096.517578125
record the last path to gazebo for showing up
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R_val = tensor(14075.6191, device='cuda:0') C_val = tensor(14453.3594, device='cuda:0') Avg Actor 14075.619140625 --- Avg Critic 14453.359375 My actor is going on the right road Hallelujah :) Updated Epoch: 0, epoch time: 59.507min, tot time: 0.041day, L_actor: 14075.619, L_critic: 14453.359, update: True Save Checkpoints epoch:1, batch:100/2500, reward:10704.439453125
record the last path to gazebo for showing up
epoch:1, batch:200/2500, reward:10656.978515625
record the last path to gazebo for showing up
epoch:1, batch:300/2500, reward:10854.521484375
record the last path to gazebo for showing up
epoch:1, batch:400/2500, reward:11199.013671875
record the last path to gazebo for showing up
epoch:1, batch:500/2500, reward:10938.876953125
record the last path to gazebo for showing up
epoch:1, batch:600/2500, reward:10641.2119140625
record the last path to gazebo for showing up
epoch:1, batch:700/2500, reward:11727.9755859375
record the last path to gazebo for showing up
epoch:1, batch:800/2500, reward:10840.509765625
record the last path to gazebo for showing up
epoch:1, batch:900/2500, reward:10888.2431640625
record the last path to gazebo for showing up
epoch:1, batch:1000/2500, reward:11065.4091796875
record the last path to gazebo for showing up
epoch:1, batch:1100/2500, reward:10550.564453125
record the last path to gazebo for showing up
epoch:1, batch:1200/2500, reward:10563.5224609375
record the last path to gazebo for showing up
epoch:1, batch:1300/2500, reward:10840.6943359375
record the last path to gazebo for showing up
epoch:1, batch:1400/2500, reward:10476.4423828125
record the last path to gazebo for showing up
epoch:1, batch:1500/2500, reward:10330.80859375
record the last path to gazebo for showing up
epoch:1, batch:1600/2500, reward:10478.955078125
record the last path to gazebo for showing up
epoch:1, batch:1700/2500, reward:10410.1396484375
record the last path to gazebo for showing up
epoch:1, batch:1800/2500, reward:10387.5703125
record the last path to gazebo for showing up
epoch:1, batch:1900/2500, reward:11003.36328125
record the last path to gazebo for showing up
epoch:1, batch:2000/2500, reward:10905.513671875
record the last path to gazebo for showing up
epoch:1, batch:2100/2500, reward:10926.8759765625
record the last path to gazebo for showing up
epoch:1, batch:2200/2500, reward:10666.625
record the last path to gazebo for showing up
epoch:1, batch:2300/2500, reward:10711.5546875
record the last path to gazebo for showing up
epoch:1, batch:2400/2500, reward:10528.876953125
record the last path to gazebo for showing up
epoch:1, batch:2500/2500, reward:10322.08984375
record the last path to gazebo for showing up
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3.4619, 4.4065, 4.2871, 3.2610, 6.0531, 6.0929, 6.1371, 6.0343,
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tensor([ 1.0317, 1.7825, 1.7363, 1.1244, -8.0072, -8.0460, -7.7906, -7.7530,
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2.0690, 2.4382, 2.6658, 2.3852, 0.6281, -0.0821, 0.0959, 0.6756,
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tensor([ 0.4904, 1.4358, 1.4155, 0.6114, -8.2749, -8.3190, -8.0990, -8.0538,
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1.5716, 2.0661, 2.2829, 1.8760, 0.5644, -0.1080, 0.0949, 0.6444,
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tensor([ 4.2445e+00, 4.3765e+00, 4.2924e+00, 4.2160e+00, -6.8480e+00,
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tensor([ 3.1735e+00, 3.3901e+00, 3.2912e+00, 3.1535e+00, -7.4984e+00,
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device='cuda:0')
tensor([ 4.7967, 5.5680, 5.6550, 4.9317, -6.4733, -6.4617, -6.0690, -6.0737,
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R_val = tensor(13824.2363, device='cuda:0')
C_val = tensor(14075.6191, device='cuda:0')
Avg Actor 13824.236328125 --- Avg Critic 14075.619140625
My actor is going on the right road Hallelujah :) Updated
Epoch: 1, epoch time: 60.007min, tot time: 0.083day, L_actor: 13824.236, L_critic: 14075.619, update: True
Save Checkpoints
epoch:2, batch:100/2500, reward:10378.54296875
record the last path to gazebo for showing up
epoch:2, batch:200/2500, reward:10356.591796875
record the last path to gazebo for showing up
epoch:2, batch:300/2500, reward:10550.681640625
record the last path to gazebo for showing up
epoch:2, batch:400/2500, reward:10179.09765625
record the last path to gazebo for showing up
epoch:2, batch:500/2500, reward:10655.490234375
record the last path to gazebo for showing up
epoch:2, batch:600/2500, reward:10424.05078125
record the last path to gazebo for showing up
epoch:2, batch:700/2500, reward:10451.947265625
record the last path to gazebo for showing up
epoch:2, batch:800/2500, reward:10455.6953125
record the last path to gazebo for showing up
epoch:2, batch:900/2500, reward:10288.9609375
record the last path to gazebo for showing up
epoch:2, batch:1000/2500, reward:10439.08984375
record the last path to gazebo for showing up
epoch:2, batch:1100/2500, reward:10515.482421875
record the last path to gazebo for showing up
epoch:2, batch:1200/2500, reward:10349.30078125
record the last path to gazebo for showing up
epoch:2, batch:1300/2500, reward:10388.091796875
record the last path to gazebo for showing up
epoch:2, batch:1400/2500, reward:10266.7509765625
record the last path to gazebo for showing up
epoch:2, batch:1500/2500, reward:10115.2177734375
record the last path to gazebo for showing up
epoch:2, batch:1600/2500, reward:10349.943359375
record the last path to gazebo for showing up
epoch:2, batch:1700/2500, reward:10138.505859375
record the last path to gazebo for showing up
epoch:2, batch:1800/2500, reward:10556.7041015625
record the last path to gazebo for showing up
epoch:2, batch:1900/2500, reward:10422.7939453125
record the last path to gazebo for showing up
epoch:2, batch:2000/2500, reward:10116.5234375
record the last path to gazebo for showing up
epoch:2, batch:2100/2500, reward:10467.40234375
record the last path to gazebo for showing up
epoch:2, batch:2200/2500, reward:10221.490234375
record the last path to gazebo for showing up
epoch:2, batch:2300/2500, reward:10107.6181640625
record the last path to gazebo for showing up
epoch:2, batch:2400/2500, reward:10374.068359375
record the last path to gazebo for showing up
epoch:2, batch:2500/2500, reward:10358.125
record the last path to gazebo for showing up
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tensor([ 2.0851, 8.6140, 8.7457, 3.5305, -8.1856, -6.7628, -7.2046, -8.3299,
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tensor([ 5.8461e+00, 8.6834e+00, 8.4019e+00, 5.4069e+00, -6.4167e-01,
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8.9733e+00, -7.8952e+00, -6.6818e+00, -9.2046e+00, -9.3798e+00],
device='cuda:0')
tensor([ 5.5336, 8.6339, 8.3510, 5.1147, -1.6685, 0.7146, -0.8761, -3.2371,
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tensor([ 6.2976, 8.8934, 8.7339, 6.0264, 1.0372, 2.0423, 1.0709, -0.2856,
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tensor([ 5.3503, 9.3273, 9.4114, 6.4531, -6.5063, -4.6319, -5.0785, -6.5980,
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tensor([ 4.3269, 8.0864, 8.4123, 5.5214, -5.0056, -6.2727, -5.4971, -4.4640,
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tensor([ 3.2219, 7.8369, 8.0091, 3.9866, -4.2506, -5.5190, -5.1177, -4.0602,
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5.2295, 6.1903, 5.4641, 4.3111, -5.1965, -4.2146, -5.7935, -7.0045,
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2.6702, 2.8737, 3.1804, 2.9250, -6.8751, -7.2636, -7.1035, -6.5204,
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tensor([ 2.7840, 7.7559, 8.2790, 4.6656, -8.0361, -8.2992, -7.9005, -7.6500,
1.1909, 1.9281, 4.0342, 3.0930, -8.0678, -7.7272, -7.2553, -7.6730,
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tensor([ 1.7172, 7.3439, 7.9503, 3.7733, -8.3971, -8.5941, -8.2792, -8.0860,
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1.1906, 1.5547, 6.9681, 6.9941], device='cuda:0')
tensor([ 2.6405, 7.6287, 7.8837, 3.5589, -4.7290, -6.2593, -5.7002, -4.3545,
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-2.5150, -2.3928, 4.1329, 4.4092], device='cuda:0')
R_val = tensor(10776.4570, device='cuda:0')
C_val = tensor(13824.2363, device='cuda:0')
Avg Actor 10776.45703125 --- Avg Critic 13824.236328125
My actor is going on the right road Hallelujah :) Updated
Epoch: 2, epoch time: 58.907min, tot time: 0.124day, L_actor: 10776.457, L_critic: 13824.236, update: True
Save Checkpoints
epoch:3, batch:100/2500, reward:9960.8525390625
record the last path to gazebo for showing up
epoch:3, batch:200/2500, reward:9853.7265625
record the last path to gazebo for showing up
epoch:3, batch:300/2500, reward:9775.9111328125
record the last path to gazebo for showing up
epoch:3, batch:400/2500, reward:9790.181640625
record the last path to gazebo for showing up
epoch:3, batch:500/2500, reward:9724.5400390625
record the last path to gazebo for showing up
epoch:3, batch:600/2500, reward:9727.103515625
record the last path to gazebo for showing up
epoch:3, batch:700/2500, reward:9766.287109375
record the last path to gazebo for showing up
epoch:3, batch:800/2500, reward:9593.86328125
record the last path to gazebo for showing up
epoch:3, batch:900/2500, reward:9675.7158203125
record the last path to gazebo for showing up
epoch:3, batch:1000/2500, reward:9626.01953125
record the last path to gazebo for showing up
epoch:3, batch:1100/2500, reward:9579.0556640625
record the last path to gazebo for showing up
epoch:3, batch:1200/2500, reward:9553.92578125
record the last path to gazebo for showing up
epoch:3, batch:1300/2500, reward:9593.15234375
record the last path to gazebo for showing up
epoch:3, batch:1400/2500, reward:9492.314453125
record the last path to gazebo for showing up
epoch:3, batch:1500/2500, reward:9455.248046875
record the last path to gazebo for showing up
epoch:3, batch:1600/2500, reward:9477.283203125
record the last path to gazebo for showing up
epoch:3, batch:1700/2500, reward:9540.16015625
record the last path to gazebo for showing up
epoch:3, batch:1800/2500, reward:9443.3046875
record the last path to gazebo for showing up
epoch:3, batch:1900/2500, reward:9494.2392578125
record the last path to gazebo for showing up
epoch:3, batch:2000/2500, reward:9419.390625
record the last path to gazebo for showing up
epoch:3, batch:2100/2500, reward:9498.205078125
record the last path to gazebo for showing up
epoch:3, batch:2200/2500, reward:9342.7734375
record the last path to gazebo for showing up
epoch:3, batch:2300/2500, reward:9416.712890625
record the last path to gazebo for showing up
epoch:3, batch:2400/2500, reward:9377.2919921875
record the last path to gazebo for showing up
epoch:3, batch:2500/2500, reward:9425.8955078125
record the last path to gazebo for showing up
tensor([-9.9586, -9.9141, -9.9112, -9.9567, -9.9977, -9.9983, -9.9981, -9.9969,
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R_val = tensor(9612.7891, device='cuda:0') C_val = tensor(10776.4570, device='cuda:0') Avg Actor 9612.7890625 --- Avg Critic 10776.45703125 My actor is going on the right road Hallelujah :) Updated Epoch: 3, epoch time: 59.170min, tot time: 0.165day, L_actor: 9612.789, L_critic: 10776.457, update: True Save Checkpoints epoch:4, batch:100/2500, reward:9225.6015625
record the last path to gazebo for showing up
epoch:4, batch:200/2500, reward:9179.076171875
record the last path to gazebo for showing up
epoch:4, batch:300/2500, reward:9051.1923828125
record the last path to gazebo for showing up
epoch:4, batch:400/2500, reward:9016.9541015625
record the last path to gazebo for showing up
epoch:4, batch:500/2500, reward:9019.4765625
record the last path to gazebo for showing up
epoch:4, batch:600/2500, reward:8912.0537109375
record the last path to gazebo for showing up
epoch:4, batch:700/2500, reward:9004.666015625
record the last path to gazebo for showing up
epoch:4, batch:800/2500, reward:9128.693359375
record the last path to gazebo for showing up
epoch:4, batch:900/2500, reward:9012.578125
record the last path to gazebo for showing up
epoch:4, batch:1000/2500, reward:8971.18359375
record the last path to gazebo for showing up
epoch:4, batch:1100/2500, reward:9029.5263671875
record the last path to gazebo for showing up
epoch:4, batch:1200/2500, reward:8884.279296875
record the last path to gazebo for showing up
epoch:4, batch:1300/2500, reward:8823.423828125
record the last path to gazebo for showing up
epoch:4, batch:1400/2500, reward:8886.8388671875
record the last path to gazebo for showing up
epoch:4, batch:1500/2500, reward:8889.63671875
record the last path to gazebo for showing up
epoch:4, batch:1600/2500, reward:8913.865234375
record the last path to gazebo for showing up
epoch:4, batch:1700/2500, reward:8777.796875
record the last path to gazebo for showing up
epoch:4, batch:1800/2500, reward:8713.7294921875
record the last path to gazebo for showing up
epoch:4, batch:1900/2500, reward:8770.447265625
record the last path to gazebo for showing up
epoch:4, batch:2000/2500, reward:8652.5419921875
record the last path to gazebo for showing up
epoch:4, batch:2100/2500, reward:8704.9033203125
record the last path to gazebo for showing up
epoch:4, batch:2200/2500, reward:8592.6298828125
record the last path to gazebo for showing up
epoch:4, batch:2300/2500, reward:8715.97265625
record the last path to gazebo for showing up
epoch:4, batch:2400/2500, reward:8605.4140625
record the last path to gazebo for showing up
epoch:4, batch:2500/2500, reward:8564.18359375
record the last path to gazebo for showing up
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2.3183, 2.7855, 1.7931, 1.4056, 8.1385, 7.9670, 8.1686, 8.2747,
8.9928, 8.5352, 8.7441, 9.1703, 8.6833, 9.5734, 9.6821, 8.9741,
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7.0060, 7.2042, 7.1699, 7.0173, 6.4593, 9.1575, 9.4684, 8.1112,
9.8542, 9.8711, 9.9140, 9.8762, 9.9369, 9.2542, 9.3369, 9.9231,
-5.5186, -2.4471, 3.7939, 3.6500], device='cuda:0')
tensor([ 5.5008, 6.8065, 7.0143, 6.4602, -9.0076, -8.8635, -8.8001, -8.9982,
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-5.7186, -4.4067, 6.1543, 5.8482], device='cuda:0')
tensor([ 5.1953, 6.8494, 7.0351, 6.2053, -9.0895, -8.9790, -8.9274, -9.0793,
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tensor([ 8.2496, 8.3380, 8.5104, 8.6943, -6.1380, -3.8930, -3.6512, -6.2434,
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tensor([ 4.4369, 9.2693, 9.3158, 5.8688, -8.5051, -8.7432, -8.7883, -8.4428,
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tensor([ 8.7762, 8.7575, 8.8676, 9.0735, -5.4953, -2.7314, -2.2332, -5.4774,
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R_val = tensor(11503.1729, device='cuda:0')
C_val = tensor(9612.7891, device='cuda:0')
Avg Actor 11503.1728515625 --- Avg Critic 9612.7890625
Epoch: 4, epoch time: 58.768min, tot time: 0.206day, L_actor: 11503.173, L_critic: 9612.789, update: False
Save Checkpoints
epoch:5, batch:100/2500, reward:8689.3515625
record the last path to gazebo for showing up
epoch:5, batch:200/2500, reward:8586.9619140625
record the last path to gazebo for showing up
epoch:5, batch:300/2500, reward:8535.044921875
record the last path to gazebo for showing up
epoch:5, batch:400/2500, reward:8560.73828125
record the last path to gazebo for showing up
epoch:5, batch:500/2500, reward:8520.18359375
record the last path to gazebo for showing up
epoch:5, batch:600/2500, reward:8480.5908203125
record the last path to gazebo for showing up
epoch:5, batch:700/2500, reward:8451.13671875
record the last path to gazebo for showing up
epoch:5, batch:800/2500, reward:8487.48046875
record the last path to gazebo for showing up
epoch:5, batch:900/2500, reward:8505.646484375
record the last path to gazebo for showing up
epoch:5, batch:1000/2500, reward:8467.3623046875
record the last path to gazebo for showing up
epoch:5, batch:1100/2500, reward:8549.8447265625
record the last path to gazebo for showing up
epoch:5, batch:1200/2500, reward:8442.8720703125
record the last path to gazebo for showing up
epoch:5, batch:1300/2500, reward:8570.193359375
record the last path to gazebo for showing up
epoch:5, batch:1400/2500, reward:8462.5927734375
record the last path to gazebo for showing up
epoch:5, batch:1500/2500, reward:8462.865234375
record the last path to gazebo for showing up
epoch:5, batch:1600/2500, reward:8560.177734375
record the last path to gazebo for showing up
epoch:5, batch:1700/2500, reward:8449.763671875
record the last path to gazebo for showing up
epoch:5, batch:1800/2500, reward:8394.732421875
record the last path to gazebo for showing up
epoch:5, batch:1900/2500, reward:8398.75
record the last path to gazebo for showing up
epoch:5, batch:2000/2500, reward:8509.265625
record the last path to gazebo for showing up
epoch:5, batch:2100/2500, reward:8506.822265625
record the last path to gazebo for showing up
epoch:5, batch:2200/2500, reward:8561.14453125
record the last path to gazebo for showing up
epoch:5, batch:2300/2500, reward:8419.9462890625
record the last path to gazebo for showing up
epoch:5, batch:2400/2500, reward:8483.05859375
record the last path to gazebo for showing up
epoch:5, batch:2500/2500, reward:8469.8642578125
record the last path to gazebo for showing up
tensor([0.9054, 0.7826, 0.7875, 0.9192, 3.4684, 3.0894, 3.0631, 3.4633, 3.8934,
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tensor([2.1764, 1.7720, 1.7720, 2.1531, 4.0080, 3.7873, 3.7143, 3.9552, 3.7960,
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tensor([2.7841, 2.2470, 2.3217, 2.8435, 5.8974, 5.7401, 5.7348, 5.9068, 5.9783,
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tensor([4.1451, 3.3967, 3.3717, 4.1228, 6.0852, 5.8110, 5.8719, 6.1399, 6.5255,
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tensor([4.9787, 4.2403, 4.2059, 4.9575, 6.7658, 6.5667, 6.6264, 6.8196, 7.0802,
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tensor([5.0370, 4.3647, 4.3748, 5.0136, 7.4061, 7.2981, 7.3096, 7.4303, 7.2097,
7.0706, 6.9995, 7.1380, 6.0172, 5.7944, 5.7859, 6.0105, 6.4245, 6.2652,
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5.1838, 5.2877, 5.1623, 5.3550, 5.5249, 5.3685, 7.2710, 7.0742, 6.9199,
7.1150], device='cuda:0')
tensor([4.7412, 4.1181, 4.1400, 4.7425, 7.1174, 7.0812, 7.0537, 7.1048, 6.6419,
6.5342, 6.4920, 6.6022, 6.4228, 6.2083, 6.1769, 6.3905, 6.8464, 6.7410,
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5.6711, 6.0273, 5.8376, 5.8071, 5.9939, 5.7348, 5.3466, 5.3424, 5.7293,
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5.1655, 5.4400, 4.7930, 4.9634, 5.1124, 4.8749, 7.0436, 6.9193, 6.7186,
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tensor([5.6784, 5.0044, 4.9606, 5.6405, 7.7185, 7.6109, 7.6001, 7.7102, 7.4787,
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5.7496, 6.0185, 5.3456, 5.5057, 5.6303, 5.4123, 7.6685, 7.5769, 7.3515,
7.4805], device='cuda:0')
tensor([5.3623, 4.5606, 4.5549, 5.3378, 7.3457, 7.3202, 7.3148, 7.3505, 6.9626,
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5.4696, 5.7234, 4.9800, 5.0435, 5.1380, 4.9996, 7.3390, 7.2335, 6.9482,
7.0840], device='cuda:0')
tensor([5.5247, 4.7530, 4.7111, 5.4856, 7.3106, 7.2771, 7.2683, 7.3163, 6.8901,
6.7743, 6.6932, 6.8132, 6.9303, 6.8163, 6.7991, 6.9140, 7.1432, 7.0800,
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5.3935, 5.6604, 4.8619, 4.8688, 4.9290, 4.8417, 7.3002, 7.1955, 6.8949,
7.0254], device='cuda:0')
R_val = tensor(13630.0967, device='cuda:0')
C_val = tensor(9612.7891, device='cuda:0')
Avg Actor 13630.0966796875 --- Avg Critic 9612.7890625
Epoch: 5, epoch time: 59.437min, tot time: 0.247day, L_actor: 13630.097, L_critic: 9612.789, update: False
Save Checkpoints
epoch:6, batch:100/2500, reward:8432.14453125
record the last path to gazebo for showing up
epoch:6, batch:200/2500, reward:8370.541015625
record the last path to gazebo for showing up
epoch:6, batch:300/2500, reward:8507.34765625
record the last path to gazebo for showing up
epoch:6, batch:400/2500, reward:8479.0634765625
record the last path to gazebo for showing up
epoch:6, batch:500/2500, reward:8498.267578125
record the last path to gazebo for showing up
epoch:6, batch:600/2500, reward:8487.630859375
record the last path to gazebo for showing up
epoch:6, batch:700/2500, reward:8474.39453125
record the last path to gazebo for showing up
epoch:6, batch:800/2500, reward:8561.318359375
record the last path to gazebo for showing up
epoch:6, batch:900/2500, reward:8612.6875
record the last path to gazebo for showing up
epoch:6, batch:1000/2500, reward:8554.8984375
record the last path to gazebo for showing up
epoch:6, batch:1100/2500, reward:8465.158203125
record the last path to gazebo for showing up
epoch:6, batch:1200/2500, reward:8459.2890625
record the last path to gazebo for showing up
epoch:6, batch:1300/2500, reward:8483.3330078125
record the last path to gazebo for showing up
epoch:6, batch:1400/2500, reward:8416.5595703125
record the last path to gazebo for showing up
epoch:6, batch:1500/2500, reward:8372.7734375
record the last path to gazebo for showing up
epoch:6, batch:1600/2500, reward:8430.8857421875
record the last path to gazebo for showing up
epoch:6, batch:1700/2500, reward:8407.1513671875
record the last path to gazebo for showing up
epoch:6, batch:1800/2500, reward:8375.3056640625
record the last path to gazebo for showing up
epoch:6, batch:1900/2500, reward:8312.490234375
record the last path to gazebo for showing up
epoch:6, batch:2000/2500, reward:8337.462890625
record the last path to gazebo for showing up
epoch:6, batch:2100/2500, reward:8548.5546875
record the last path to gazebo for showing up
epoch:6, batch:2200/2500, reward:8312.76171875
record the last path to gazebo for showing up
epoch:6, batch:2300/2500, reward:8313.3046875
record the last path to gazebo for showing up
epoch:6, batch:2400/2500, reward:8313.2734375
record the last path to gazebo for showing up
epoch:6, batch:2500/2500, reward:8373.0341796875
record the last path to gazebo for showing up
tensor([ 3.7219e+00, 2.6849e+00, 2.4459e+00, 3.7025e+00, -7.7189e+00,
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1.1127e+00, -5.5527e+00, -4.3830e+00, -2.6045e+00, -3.5404e+00],
device='cuda:0')
tensor([ 1.8690, 2.0681, 2.1324, 2.0867, -7.8236, -7.3807, -7.1565, -7.6187,
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tensor([-0.8852, 0.1113, 0.3251, -0.8336, -4.3889, -3.8072, -3.7514, -4.3118,
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tensor([ 1.8985, 2.2766, 2.4665, 2.1007, -6.7684, -6.2967, -6.0839, -6.5467,
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tensor([ 3.1745, 2.5136, 2.6006, 3.3153, -6.8085, -6.1965, -5.8334, -6.4579,
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tensor([ 2.1906, 2.8568, 2.9892, 2.2819, -3.9339, -3.3062, -3.0699, -3.6873,
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tensor([ 4.1931, 3.6878, 3.7494, 4.2955, -5.4865, -4.9375, -4.4251, -4.9755,
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tensor([ 6.0335, 5.8172, 5.8658, 6.0972, -0.9964, -0.2287, 0.1454, -0.6199,
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1.2409, 1.1104, -0.4166, 1.0308], device='cuda:0')
tensor([ 7.0206, 5.8783, 5.8536, 6.9892, -0.2565, 0.5466, 1.2292, 0.4491,
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tensor([ 5.9109, 5.1678, 5.3069, 5.9701, -2.8626, -2.2351, -1.7588, -2.3700,
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tensor([7.5785, 6.1099, 6.1225, 7.6254, 3.7545, 4.1580, 4.7502, 4.3495, 5.9180,
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tensor([6.8859, 6.4956, 6.5887, 6.9157, 1.7426, 2.4656, 2.8904, 2.2026, 4.4782,
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tensor([7.1566, 5.8101, 5.7571, 7.1425, 3.4096, 3.7703, 4.4290, 3.9831, 4.8082,
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tensor([7.7866, 7.0906, 7.1022, 7.7730, 3.3139, 3.9790, 4.3529, 3.6975, 5.5064,
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tensor([7.0055, 5.5366, 5.5480, 7.0696, 2.4381, 2.8289, 3.4794, 3.0629, 4.4139,
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tensor([7.6874, 7.1124, 7.1189, 7.6953, 1.0379, 1.7783, 2.2474, 1.5109, 4.2948,
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tensor([6.8437, 5.3227, 5.2605, 6.8584, 2.7551, 3.1280, 3.7708, 3.3257, 3.6969,
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tensor([7.0604, 5.7487, 5.6802, 7.0478, 3.1973, 3.6379, 4.2704, 3.7504, 4.4898,
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tensor([7.8770, 6.5100, 6.5396, 7.9283, 4.4741, 4.8962, 5.3438, 4.9609, 6.4713,
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8.9823], device='cuda:0')
R_val = tensor(18800.3613, device='cuda:0')
C_val = tensor(9612.7891, device='cuda:0')
Avg Actor 18800.361328125 --- Avg Critic 9612.7890625
Epoch: 6, epoch time: 59.297min, tot time: 0.288day, L_actor: 18800.361, L_critic: 9612.789, update: False
Save Checkpoints
epoch:7, batch:100/2500, reward:8422.82421875
record the last path to gazebo for showing up
epoch:7, batch:200/2500, reward:8388.9951171875
record the last path to gazebo for showing up
epoch:7, batch:300/2500, reward:8320.177734375
record the last path to gazebo for showing up
epoch:7, batch:400/2500, reward:8275.8544921875
record the last path to gazebo for showing up
epoch:7, batch:500/2500, reward:8398.1455078125
record the last path to gazebo for showing up
epoch:7, batch:600/2500, reward:8306.51171875
record the last path to gazebo for showing up
epoch:7, batch:700/2500, reward:8260.4912109375
record the last path to gazebo for showing up
epoch:7, batch:800/2500, reward:8287.16015625
record the last path to gazebo for showing up
epoch:7, batch:900/2500, reward:8287.0390625
record the last path to gazebo for showing up
epoch:7, batch:1000/2500, reward:8320.578125
record the last path to gazebo for showing up
epoch:7, batch:1100/2500, reward:8267.4326171875
record the last path to gazebo for showing up
epoch:7, batch:1200/2500, reward:8253.18359375
record the last path to gazebo for showing up
epoch:7, batch:1300/2500, reward:8300.12890625
record the last path to gazebo for showing up
epoch:7, batch:1400/2500, reward:8281.5810546875
record the last path to gazebo for showing up
epoch:7, batch:1500/2500, reward:8357.77734375
record the last path to gazebo for showing up
epoch:7, batch:1600/2500, reward:8358.041015625
record the last path to gazebo for showing up
epoch:7, batch:1700/2500, reward:8267.220703125
record the last path to gazebo for showing up
epoch:7, batch:1800/2500, reward:8276.853515625
record the last path to gazebo for showing up
epoch:7, batch:1900/2500, reward:8313.27734375
record the last path to gazebo for showing up
epoch:7, batch:2000/2500, reward:8372.2060546875
record the last path to gazebo for showing up
epoch:7, batch:2100/2500, reward:8365.0185546875
record the last path to gazebo for showing up
epoch:7, batch:2200/2500, reward:8354.89453125
record the last path to gazebo for showing up
epoch:7, batch:2300/2500, reward:8252.28125
record the last path to gazebo for showing up
epoch:7, batch:2400/2500, reward:8225.453125
record the last path to gazebo for showing up
epoch:7, batch:2500/2500, reward:8494.720703125
record the last path to gazebo for showing up
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R_val = tensor(14425.3242, device='cuda:0') C_val = tensor(9612.7891, device='cuda:0') Avg Actor 14425.32421875 --- Avg Critic 9612.7890625 Epoch: 7, epoch time: 58.994min, tot time: 0.329day, L_actor: 14425.324, L_critic: 9612.789, update: False Save Checkpoints epoch:8, batch:100/2500, reward:8290.2958984375
record the last path to gazebo for showing up
epoch:8, batch:200/2500, reward:8219.1640625
record the last path to gazebo for showing up
epoch:8, batch:300/2500, reward:8204.271484375
record the last path to gazebo for showing up
epoch:8, batch:400/2500, reward:8239.5869140625
record the last path to gazebo for showing up
epoch:8, batch:500/2500, reward:8281.427734375
record the last path to gazebo for showing up
epoch:8, batch:600/2500, reward:8252.69140625
record the last path to gazebo for showing up
epoch:8, batch:700/2500, reward:8257.9296875
record the last path to gazebo for showing up
epoch:8, batch:800/2500, reward:8247.5625
record the last path to gazebo for showing up
epoch:8, batch:900/2500, reward:8219.76171875
record the last path to gazebo for showing up
epoch:8, batch:1000/2500, reward:8233.009765625
record the last path to gazebo for showing up
epoch:8, batch:1100/2500, reward:8274.884765625
record the last path to gazebo for showing up
epoch:8, batch:1200/2500, reward:8268.2578125
record the last path to gazebo for showing up
epoch:8, batch:1300/2500, reward:8347.2431640625
record the last path to gazebo for showing up
epoch:8, batch:1400/2500, reward:8203.5654296875
record the last path to gazebo for showing up
epoch:8, batch:1500/2500, reward:8211.9140625
record the last path to gazebo for showing up
epoch:8, batch:1600/2500, reward:8217.9375
record the last path to gazebo for showing up
epoch:8, batch:1700/2500, reward:8283.548828125
record the last path to gazebo for showing up
epoch:8, batch:1800/2500, reward:8237.6044921875
record the last path to gazebo for showing up
epoch:8, batch:1900/2500, reward:8280.8740234375
record the last path to gazebo for showing up
epoch:8, batch:2000/2500, reward:8267.490234375
record the last path to gazebo for showing up
epoch:8, batch:2100/2500, reward:8274.8017578125
record the last path to gazebo for showing up
epoch:8, batch:2200/2500, reward:8342.662109375
record the last path to gazebo for showing up
epoch:8, batch:2300/2500, reward:8270.248046875
record the last path to gazebo for showing up
epoch:8, batch:2400/2500, reward:8260.5244140625
record the last path to gazebo for showing up
epoch:8, batch:2500/2500, reward:8286.9912109375
record the last path to gazebo for showing up
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tensor([7.5792, 5.8492, 6.1885, 7.7869, 9.3678, 9.4247, 9.4578, 9.4007, 9.2625,
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R_val = tensor(14584.9238, device='cuda:0')
C_val = tensor(9612.7891, device='cuda:0')
Avg Actor 14584.923828125 --- Avg Critic 9612.7890625
Epoch: 8, epoch time: 59.458min, tot time: 0.371day, L_actor: 14584.924, L_critic: 9612.789, update: False
Save Checkpoints
epoch:9, batch:100/2500, reward:8417.978515625
record the last path to gazebo for showing up
epoch:9, batch:200/2500, reward:8288.435546875
record the last path to gazebo for showing up
epoch:9, batch:300/2500, reward:8252.3203125
record the last path to gazebo for showing up
epoch:9, batch:400/2500, reward:8205.958984375
record the last path to gazebo for showing up
epoch:9, batch:500/2500, reward:8205.716796875
record the last path to gazebo for showing up
epoch:9, batch:600/2500, reward:8149.53857421875
record the last path to gazebo for showing up
epoch:9, batch:700/2500, reward:8183.59228515625
record the last path to gazebo for showing up
epoch:9, batch:800/2500, reward:8181.5732421875
record the last path to gazebo for showing up
epoch:9, batch:900/2500, reward:8286.8203125
record the last path to gazebo for showing up
epoch:9, batch:1000/2500, reward:8204.35546875
record the last path to gazebo for showing up
epoch:9, batch:1100/2500, reward:8200.62890625
record the last path to gazebo for showing up
epoch:9, batch:1200/2500, reward:8206.6484375
record the last path to gazebo for showing up
xxxxxxxxxximport torchindex = int(9.34)table = [[1,2],[3,4]]table_1 = [[3,44],[5,6]]table = torch.as_tensor(table)table_1 = torch.as_tensor(table_1)result = torch.maximum(table_1[:,0],table_1[:,1])print(result)import torcht = torch.ones(10,3,2)t[:,:,0] +=2print(t)